PhD Studentship in Artificial Intelligence for Structure-Based Virtual Screening Imperial College London in United Kingdom

This PhD studentship at Imperial College London offers an exciting opportunity to advance artificial intelligence (AI) methods for structure-based virtual screening in drug discovery. The project focuses on developing predictive models that leverage atomic-resolution structures of macromolecular targets and molecular activity data to identify new drug leads. By computationally screening vast libraries of molecules, these AI models aim to accelerate the discovery of therapeutics for various diseases.

Despite notable successes, current AI approaches face significant challenges, such as augmenting training datasets to improve model performance and predicting how models behave outside their applicability domain. This research will address these issues using both synthetic and real datasets, contributing to the broader field of supervised learning and computational drug design.

The successful candidate will join the Ballester Group, led by Dr. Pedro Ballester, within the Department of Bioengineering at Imperial College London. The group is internationally recognized for its work in AI-driven drug discovery, and relevant publications can be found at https://ballestergroup.github.io/. The research environment is stimulating and collaborative, offering access to cutting-edge resources and expertise.

Funding for the studentship includes living expenses at an enhanced tax-free rate of £23,805 per year and full PhD tuition fees of £31,100 per year, guaranteed for three years with the possibility of extension to a fourth year. The project is ideal for motivated scientists with a strong background in computational data analysis, machine learning, and bioengineering.

Applicants must hold university degree(s) in a relevant area, have completed courses in machine learning applied to scientific problems, and demonstrate excellent academic performance, particularly in research projects focused on computational data analysis. Proficiency in Python or R is essential, and candidates must meet Imperial College London’s English language requirements (link). Desirable skills include experience with chemical informatics toolkits (e.g., RDKit, OpenBabel), machine learning platforms (e.g., DeepChem, TorchDrug, Scikit-Learn, Caret), structural biology databases (e.g., PDBe, AlphaFold, PDBbind), medicinal chemistry databases (e.g., ChEMBL, SureChEMBL, PubChem, ZINC), and computational chemistry software (e.g., Vina, DOCK).

To apply, candidates should email Dr. Ballester at [email protected] with a CV, grades for each completed university degree, and a covering letter (maximum two pages) explaining how they meet the essential and desirable criteria and how the position aligns with their career plans. The email should also include the names and email addresses of two academic referees and use the subject line 'PhD in AI for SBVS'. Please mention where you saw the position advertised.

Imperial College London is consistently ranked among the top universities globally, and London is recognized as the best city for university students (link). This studentship offers a unique opportunity to contribute to impactful research in AI and drug discovery while enjoying a vibrant academic and cultural environment.